library(rgeos)
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library(tigris)
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## plot
library(dplyr)
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library(leaflet)
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library(sp)
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library(ggmap)
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library(maptools)
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library(broom)
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library(httr)
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library(rgdal)
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library(tidyverse)
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# Create a data frame that has the unique name, latitude, and longitude for each Citibike station that was present in the system in July 2014
load("~/coursework/week1/trips.RData")
citibike_stations <- trips %>% filter(ymd > "2014-06-30" & ymd < "2014-08-01") %>% select(start_station_name, start_station_latitude, start_station_longitude) %>% arrange(desc(start_station_latitude)) %>% unique()
colnames(citibike_stations)[1] <- "station_name"
colnames(citibike_stations)[2] <- "latitude"
colnames(citibike_stations)[3] <- "longitude"
# Make a map showing the location of each Citibike station using ggmap
nyc_map <- get_map(location = c(lon = -73.98766, lat = 40.72595), maptype = "terrain", zoom = 12)
## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=40.72595,-73.98766&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
ggmap(nyc_map) + geom_point(data=citibike_stations, aes(x=longitude, y = latitude))
# Do the same using leaflet, adding a popup that shows the name of the station when it's clicked on
leaflet(citibike_stations) %>%
addTiles() %>%
addMarkers(~longitude, ~latitude, popup = ~station_name, data = citibike_stations) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-73.98, 40.75, zoom = 13)
# Then do a spatial join to combine this data frame with the Pediacities NYC neighborhood shapefile data
r <- GET('http://data.beta.nyc//dataset/0ff93d2d-90ba-457c-9f7e-39e47bf2ac5f/resource/35dd04fb-81b3-479b-a074-a27a37888ce7/download/d085e2f8d0b54d4590b1e7d1f35594c1pediacitiesnycneighborhoods.geojson')
nyc_neighborhoods <- readOGR(content(r,'text'), 'OGRGeoJSON', verbose = F)
## No encoding supplied: defaulting to UTF-8.
stations_spdf <- citibike_stations
coordinates(stations_spdf) <- ~longitude + latitude
proj4string(stations_spdf) <- proj4string(nyc_neighborhoods)
matches <- over(stations_spdf, nyc_neighborhoods)
stations <- cbind(citibike_stations, matches)
# Make a map showing the number of unique Citibike stations in each neighborhood
# First do this using ggmap where the fill color encodes the number of stations
points_by_neighborhood <- stations %>%
group_by(neighborhood) %>%
summarize(num_points=n())
plot_data <- tidy(nyc_neighborhoods, region = "neighborhood") %>%
left_join(., points_by_neighborhood, by=c("id"="neighborhood")) %>%
filter(!is.na(num_points))
## Warning: Column `id`/`neighborhood` joining character vector and factor,
## coercing into character vector
ggmap(nyc_map) +
geom_polygon(data=plot_data,
aes(x=long, y=lat, group=group, fill=num_points), alpha=0.75)
# Then do the same using leaflet, adding a popup that shows the number of stations in a neighborhood when its shape is clicked on
map_data <- geo_join(nyc_neighborhoods, points_by_neighborhood, "neighborhood", "neighborhood")
pal <- colorNumeric(palette = "RdBu",
domain = range(map_data@data$num_points, na.rm=T))
leaflet(map_data) %>%
addTiles() %>%
addPolygons(fillColor = ~pal(num_points), popup = ~as.character(num_points)) %>%
addProviderTiles("CartoDB.Positron") %>%
setView(-73.98, 40.75, zoom = 13)
# Now create a new data frame that has the total number of trips that depart from each station at each hour of the day on July 14th
trips_by_hour <- trips %>% filter(ymd == "2014-07-14") %>% mutate(hour = format(as.POSIXct(starttime) ,format = "%H"))
colnames(trips_by_hour)[6] <- "latitude"
colnames(trips_by_hour)[7] <- "longitude"
# Do a spatial join to combine this data frame with the Pediacities NYC neighborhood shapefile data
trips_spdf <- trips_by_hour
coordinates(trips_spdf) <- ~longitude + latitude
proj4string(trips_spdf) <- proj4string(nyc_neighborhoods)
matches2 <- over(trips_spdf, nyc_neighborhoods)
trips_neighborhood <- cbind(trips_by_hour, matches2)
trips_neighborhood <- trips_neighborhood %>% group_by(start_station_name, longitude, latitude, hour, neighborhood, boroughCode,borough,X.id) %>% summarise(num_trips = n()) %>% filter(hour == "09" | hour == "13" | hour == "17" | hour == "22")
# Make a ggmap plot showing the number of trips that leave from each neighborhood at 9am, 1pm, 5pm, and 10pm, faceted by hour, where each facet contains a map where the fill color encodes the number of departing trips in each neighborhood
ggmap(nyc_map) +
geom_polygon(data=trips_neighborhood,
aes(x=longitude, y=latitude, fill=num_trips), alpha=0.75) + facet_wrap(~hour)
